Takagi-Sugeno Bulanık Cohen-Grossberg Tipi Zaman Gecikmeli Yapay Sinir Ağlarında Kararlılık Analizi

Bu çalışma çoklu zaman gecikmeli Takagi-Sugeno Bulanık Cohen-Grossberg tipi yapay sinir ağlarının global asimtotik kararlılık problemi ile ilgilenmektedir. Uygun bulanık Lyapunov fonksiyonelleri kullanılarak ve aktivasyon fonksiyonlarının Lipschitz olduğu dikkate alnarak, gecikmeli Takagi-Sugeno Bulanık Cohen-Grossberg yapay sinir ağlarında denge noktasının global asimtotik gecikme parametrelerinden bağımsız olarak, yeni yeterli bir kararlılık koşulu sunulmuştur. Elde edilen koşul sadece sinir ağının sistem parametrelerine bağlı olarak ifade edilmiştir. Bu nedenle, bu çalışmada çalışılan yapay sinir ağı modelinin denge ve kararlılık özellikleri, bazı özel matris sınıflarının temel özellikleri kullanarak kolaylıkla doğrulanabilir.

STABILITY ANALYSIS OF A CLASS OF TAKAGI-SUGENO FUZZY COHEN-GROSSBERG NEURAL NETWORKS WITH TIME DELAYS

This paper deals with the problem of the global asymptotic stability of the class of TakagiSugeno Fuzzy Cohen-Grossberg neural networks with multiple time delays. By constructing a suitablefuzzy Lyapunov functional, we present a new delay-independent sufficient condition for the globalasymptotic stability of the equilibrium point for delayed Takagi-Sugeno Fuzzy Cohen-Grossberg neuralnetworks with respect to the Lipschitz activation functions. The obtained condition simply relies on thenetwork parameters of the neural system. Therefore, the equilibrium and stability properties of the neuralnetwork model considered in this paper can be easily verified by exploiting some basic properties of somecertain classes of matrices.

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